Date of Award

2013

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Salem, Saeed

Description

Identifying communities from networks has been a subject of great interest in Biological and Social network analysis. Finding communities can help with tasks such as identifying and fighting disease. Using graphs to represent networks and identifying dense subgraphs as communities within these graphs is an increasingly important area of study. Many of the same entities can be found in multiple networks, each representing a different type of relationship. These graphs capturing different relationships between the same entities can be combined into a single graph called a “multi-layered graph”. By finding dense subgraphs containing the same entities within multiple layers of the multi-layered graph, we can increase the confidence these dense subgraphs are communites. This paper has developed an algorithm that takes multi-layered graphs and employs quasi-clique based community discovery for extracting communities. Experimental results on real co-authorship networks show that the proposed approach discover communities that have dense interactions.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/23023

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